In the past, researchers have been unable to capture data concerning race and ethnicity in population-level traffic safety research. A recent study featuring PISC Senior Scholar Allison E. Curry, PhD, MPH, implements the Bayesian Improved Surname Geocoding (BISG) algorithm using data from the New Jersey Safety and Health Outcomes data warehouse. The BISG allows the team to effectively understand race and ethnic disparities and inequities in transportation outcomes. To test the effectiveness of the BISG, the team analyzed average monthly police-reported crash rates in 2017. Results indicate outstanding concordance between race and ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. However, results show poor concordance for American Indian/Alaskan Native drivers and multiracial drivers. Monthly crash rates among all transportation employees are similar. In the future, BISG has the potential to give researchers more insight into racial and ethnic disparities and inequities in transportation.